minesh1291

import json
import numpy as np
from pycocotools import mask
from skimage import measure

ground_truth_binary_mask = np.array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
                                     [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],

jph00

# I added all the imports by hand
import torch
from torchvision.datasets import ImageFolder
from torchvision import transforms,models
from torch import nn,optim

# For all functions including this one, I wrote the name and docstring, and sometimes also the param names
def finetune(folder, model):
    """fine tune pytorch model using images from folder and report results on validation set"""
    if not os.path.exists(folder): raise ValueError(f"{folder} does not exist")

mlaves

epignatelli

"""
An implementation of the pytorch Subset that returns an instance of the original dataset with a reduced number of items.
This has two benefits:
  - It allows to stil access the attributes of the Dataset class, such as methods, or properties.
  - You can use the usual python index notation with slices to chunk the dataset, rather than creating a list of indices

"""
class Dataset(object):
  def __init__(self, iterable):
    self.items = iterable

danallison

from sshtunnel import SSHTunnelForwarder
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from functools import wraps

# secrets.py contains credentials, etc.
import secrets

def get_engine_for_port(port):
    return create_engine('postgresql://{user}:{password}@{host}:{port}/{db}'.format(

jeremyjordan

from keras.callbacks import Callback
import keras.backend as K
import numpy as np

class SGDRScheduler(Callback):
    '''Cosine annealing learning rate scheduler with periodic restarts.

    # Usage
        ```python
            schedule = SGDRScheduler(min_lr=1e-5,

jayspeidell

# Info on how to get your api key (kaggle.json) here: https://github.com/Kaggle/kaggle-api#api-credentials
!pip install kaggle
api_token = {"username":"USERNAME","key":"API_KEY"}
import json
import zipfile
import os
with open('/content/.kaggle/kaggle.json', 'w') as file:
    json.dump(api_token, file)
!chmod 600 /content/.kaggle/kaggle.json
!kaggle config path -p /content

mmngreco

Methods to improve Time series forecast (including ARIMA, Holt's winter)

By: Gurchetan Singh

SOURCE: https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/

Introduction

santi-pdp

kmclaugh

import pandas as pd
import numpy as np
import seaborn as sns

from keras.layers import Dense
from keras.models import Model, Sequential
from keras import initializers

## ---------- Create our linear dataset ---------------
## Set the mean, standard deviation, and size of the dataset, respectively